library(qs)
library(Seurat)
library(tidyverse)
library(miloDE)
library(sessioninfo)
library(EnhancedVolcano)
library(pals)
library(scMisc)Reproducing the figures of the manuscript
Load libraries
Figure 1
UMAP of all cells (Figure 1B)
options(timeout = 3600)
if (!file.exists("umap_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"umap_figure.qs"
)
}
umap_figure <- qread("umap_figure.qs")
DimPlot(
umap_figure,
reduction = "umap.scvi.full",
pt.size = .1,
alpha = .1,
cols = umap_figure@misc$cluster_col,
label = TRUE,
raster = FALSE
) +
NoLegend() +
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
) +
xlab("UMAP_1") +
ylab("UMAP_2")
Figure 2
UMAP immune cells (Figure 2A)
options(timeout = 3600)
if (!file.exists("ic_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"ic_figure.qs"
)
}
ic_figure <- qread("ic_figure.qs")
DimPlot(
ic_figure,
reduction = "umap.rpca",
pt.size = .1,
alpha = .3,
cols = ic_figure@misc$ic_cluster_col,
label = TRUE,
raster = FALSE
) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
) +
NoLegend() +
xlab("UMAP1") +
ylab("UMAP2")
Feature plots immune cells (Figure 2B)
FeaturePlot(
ic_figure,
features = c("MS4A7", "CX3CR1", "TREM2", "LYVE1", "FOLR2", "TIMD4"),
reduction = "umap.rpca",
pt.size = 0.1,
raster = FALSE,
coord.fixed = TRUE,
cols = c("#F0F0F0", "#CB181D"),
order = TRUE,
ncol = 3
) &
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
) &
xlab("UMAP1") &
ylab("UMAP2")
Enrichment of gene ontology terms in Macro 18 (Figure 2C)
options(timeout = 3600)
if (!file.exists("enrichr_macro18.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"b_plasma_figure.qs"
)
}
enrichr_macro18 <- qread("enrichr_macro18.qs")
enrichr_macro18 |>
dplyr::slice_min(order_by = Adjusted.P.value, n = 10, with_ties = FALSE) |>
tidyr::separate(Overlap, into = c("overlap1", "overlap2")) |> # separate overlap in two columns
dplyr::mutate(Term = gsub(x = Term, pattern = "\\s\\(.+\\)", replacement = "")) |>
dplyr::mutate(overlap = as.numeric(overlap1) / as.numeric(overlap2)) |> # calculcate overlap
ggplot(aes(y = reorder(Term, -log10(Adjusted.P.value)), x = -log10(Adjusted.P.value))) +
geom_col(fill = scales::hue_pal()(5)[1]) +
labs(
x = "-Log10 Adjusted P value",
y = ""
) +
theme_classic() +
theme(legend.position = "none")
Dot plot IGH chain genes (Figure 2E)
options(timeout = 3600)
if (!file.exists("ic_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"b_plasma_figure.qs"
)
}
b_plasma_figure <- qread("b_plasma_figure.qs")
DotPlot(
b_plasma_figure,
features = c("IGHM", "IGHD", "IGHG1", "IGHG2", "IGHG3", "IGHG4", "IGHA1", "IGHA2"),
scale = FALSE,
dot.scale = 10
) +
viridis::scale_color_viridis(option = "viridis") +
theme(axis.text.x = element_text(angle = 90, hjust = 1, vjust = 0.5, face = "italic")) +
xlab("") +
ylab("")Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Figure 3
Propeller abundance PNP vs CTRL (Figure 3A)
options(timeout = 3600)
if (!file.exists("propeller_PNP_CTRL.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"propeller_PNP_CTRL.qs"
)
}
if (!file.exists("propeller_PNP_CTRL.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"propeller_PNP_CTRL_ic.qs"
)
}
propeller_PNP_CTRL <- qread("propeller_PNP_CTRL.qs")
propeller_PNP_CTRL_ic <- qread("propeller_PNP_CTRL_ic.qs")
# function to plot custom dotplot
dotplotPropeller <- function(data, color, title) {
ggplot(data, aes(x = log2ratio, y = fct_reorder(cluster, log2ratio), color = cluster)) +
geom_point(size = 5) +
theme_classic() +
geom_vline(
xintercept = 0, color = "red",
linetype = "solid"
) +
scale_color_manual(values = color) +
xlab("Log2 fold change") +
ylab(NULL) +
theme(legend.position = "none") +
ggtitle(title)
}
dotplotPropeller(propeller_PNP_CTRL, color = umap_figure@misc$cluster_col, title = "PNP vs CTRL main clusters")
dotplotPropeller(propeller_PNP_CTRL_ic, color = ic_figure@misc$ic_cluster_col, title = "PNP vs CTRL immune cells")
Covarying Neighborhood Analysis (Figure 3B)
options(timeout = 3600)
if (!file.exists("cna_pnp_gratio_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"cna_pnp_gratio_figure.qs"
)
}
if (!file.exists("cna_incat_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"cna_incat_figure.qs"
)
}
cna_pnp_gratio_figure <- qread("cna_pnp_gratio_figure.qs")
cna_incat_figure <- qread("cna_incat_figure.qs")
# function to plot CNA feature plot
fplotCNA <- function(object, feature, title) {
FeaturePlot(
object,
reduction = "umap.scvi.full",
features = feature,
pt.size = 0.1,
order = FALSE,
coord.fixed = TRUE,
raster = FALSE,
alpha = 0.2
) +
scale_colour_gradient2(
low = "#2166AC",
mid = "white",
high = "#B2182B",
midpoint = 0,
) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank(),
) +
labs(
title = title, color = "Correlation", x = "UMAP1", y= "UMAP2"
)
}
fplotCNA(cna_pnp_gratio_figure, "cna_ncorrs_pnp", title = "PNP")Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
fplotCNA(cna_incat_figure, "cna_ncorrs", title = "INCAT")Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
fplotCNA(cna_pnp_gratio_figure, "cna_ncorrs_gratio", title = "g-ratio")Scale for colour is already present.
Adding another scale for colour, which will replace the existing scale.
Number of DEGs (Figure 3C)
options(timeout = 3600)
if (!file.exists("pnp_ctrl_pseudobulk_de.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"pnp_ctrl_pseudobulk_de.qs"
)
}
pnp_ctrl_pseudobulk <- qread("pnp_ctrl_pseudobulk_de.qs")
pnp_ctrl_pseudobulk |>
mutate(cluster = fct_reorder(cluster, n)) |>
ggplot(aes(x = cluster, y = n, fill = cluster)) +
geom_col() +
coord_flip() +
scale_fill_manual(values = umap_figure@misc$cluster_col) +
theme_classic() +
theme(legend.position = "none") +
labs(
x = "",
y = "",
title = "PNP vs CTRL"
)
MiloDE DEGs PNP vs CTRL (Figure 3D)
options(timeout = 3600)
if (!file.exists("milo_figure.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"milo_figure.qs"
)
}
milo_figure <- qread("milo_figure.qs")
plotMiloDE <- function(condition, title) {
plot <-
plot_milo_by_single_metric(
milo_figure$obj,
milo_figure$stat[[condition]],
colour_by = "n_DE_genes",
layout = "UMAP.SCVI.FULL",
size_range = c(0.5, 5),
edge_width = c(0.1, 1.0),
edge_weight.thres = 10
) +
viridis::scale_fill_viridis(name = "# DE genes", option = "inferno") +
ggtitle(title)
print(plot)
}
plotMiloDE("pnp", "PNP vs CTRL")Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Pseudobulk DE PNP vs CTRL (Figure 3E)
options(timeout = 3600)
if (!file.exists("de_pseudo_pnp_ctrl.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"de_pseudo_pnp_ctrl.qs"
)
}
de_pseudo_pnp_ctrl <- qread("de_pseudo_pnp_ctrl.qs")
# volcano plot function
volcanoPlot <- function(cluster, input, FCcutoff = 2, selectLab = NULL, drawConnectors = TRUE, condition1, condition2) {
input <- input[[cluster]]
volcano <- EnhancedVolcano::EnhancedVolcano(
data.frame(input),
lab = paste0("italic('", input[["gene"]], "')"),
x = "avg_logFC",
y = "p_val_adj",
xlim = c(min(input[["avg_logFC"]], max(input[["avg_logFC"]]))),
ylim = c(0, max(-log10(input[["p_val_adj"]]))),
pCutoff = 0.1,
FCcutoff = FCcutoff,
axisLabSize = 15,
pointSize = 2,
labSize = 5,
subtitle = NULL,
caption = NULL,
border = "full",
gridlines.major = FALSE,
gridlines.minor = FALSE,
drawConnectors = drawConnectors,
lengthConnectors = unit(0.0001, "npc"),
title = paste(condition1, "vs", condition2, "in ", cluster),
boxedLabels = TRUE,
selectLab = selectLab,
xlab = bquote(~ Log[2] ~ "fold change"),
ylab = bquote(~ -Log[10] ~ "adjusted p-value"),
parseLabels = TRUE,
legendLabels = c(
"NS", "logFC",
"p-val", "p-val + logFC"
),
legendPosition = "right",
)
}
# define clusters of interest
cluster_de <- c("mySC", "nmSC", "repairSC", "PC2")
# define genes of interests
lab_pnp_ctrl <- list(
"mySC" = paste0("italic('", c("DCN", "TNXB", "COL1A1", "COL15A1", "CD53", "IL4R", "CD74"), "')"),
"nmSC" = paste0("italic('", c("IL10RA", "IL13RA1", "CSF2RA", "TGFBI"), "')"),
"repairSC" = paste0("italic('", c("GALR1", "TMEM47"), "')"),
"PC2" = paste0("italic('", c("MFAP5", "NLGN4Y", "PCDH11Y", "IFIT3", "OASL", "MX1"), "')")
)
# plot volcano plots of selected clusters
purrr::walk(
cluster_de,
function(cluster) {
print(volcanoPlot(
cluster = cluster,
input = de_pseudo_pnp_ctrl,
FCcutoff = 2,
condition1 = "PNP",
condition2 = "CTRL",
selectLab = lab_pnp_ctrl[[cluster]]
))
}
)
Figure 4
Abundance of clusters per PNP subtype (Figure 4A)
options(timeout = 3600)
if (!file.exists("abundance_main_clusters.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"abundanance_main_clusters.qs"
)
}
abundance_main_clusters <- qread("abundance_main_clusters.qs")
abundance_main_clusters |>
ggplot() +
geom_col(
aes(x = type, y = count, fill = cell),
color = "black",
linewidth = 0.1,
position = "fill"
) +
scale_fill_manual(values = umap_figure@misc$cluster_col) +
guides(fill = guide_legend(title = NULL)) +
theme_classic() +
ylab("Proportion of cells") +
xlab("") +
theme(axis.text.x = element_text(
angle = 90,
hjust = 1, vjust = 0.3
))
Propeller abundance of PNP subtypes main cluster (Figure 4B)
options(timeout = 3600)
if (!file.exists("propeller_PNP_subtypes_main.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"propeller_PNP_subtypes_main.qs"
)
}
propeller_PNP_subtypes_main <- qread("propeller_PNP_subtypes_main.qs")
purrr::walk(
c("VN", "CIDP", "CIAP"),
function(condition) {
print(dotplotPropeller(
propeller_PNP_subtypes_main[[condition]],
color = umap_figure@misc$cluster_col,
title = paste0("CTRL vs ", condition, " main")
))
}
)
Propeller abundance of PNP subtypes immune cluster (Figure 4C)
options(timeout = 3600)
if (!file.exists("propeller_PNP_subtypes_ic.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"propeller_PNP_subtypes_ic.qs"
)
}
propeller_PNP_subtypes_ic <- qread("propeller_PNP_subtypes_ic.qs")
purrr::walk(
c("VN", "CIDP", "CIAP"),
function(condition) {
print(dotplotPropeller(
propeller_PNP_subtypes_ic[[condition]],
color = umap_figure@misc$cluster_col,
title = paste0("CTRL vs ", condition, " immune")
))
}
)
Manual PTPRC quantif (Figure 4F)
options(timeout = 3600)
if (!file.exists("manual_xenium_quantification.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"manual_xenium_quantification.qs"
)
}
manual_xenium_quantification <- qread("manual_xenium_quantification.qs")
plotQuanti <- function(y_value, title) {
plot <-
manual_xenium_quantification |>
ggplot(aes(x = level2, y = .data[[y_value]], fill = level2)) +
geom_boxplot() +
geom_jitter(height = 0, width = 0.1) +
scale_fill_manual(values = umap_figure@misc$level2_cols) +
guides(fill = guide_legend(title = NULL)) +
theme_classic() +
xlab("") +
ylab("PTPRC density") +
ggtitle(title) +
theme(
legend.position = "none",
axis.text.x = element_text(
angle = 90,
hjust = 1,
vjust = 0.3,
)
)
print(plot)
}
purrr::walk2(
c("endoPTPRC_density_sum", "epiPTPRC_density"),
c("endoneurial", "epineurial"),
function(y_value, title) {
plotQuanti(y_value = y_value, title = title)
}
)
MiloDE DEGs PNP subtypes(Figure 4G)
purrr::walk2(
c("vn", "cidp", "ciap"),
c("VN vs CTRL", "CIDP vs CTRL", "CIAP vs CTRL"),
function(condition, title) {
plotMiloDE(condition, title)
}
)Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Scale for fill is already present.
Adding another scale for fill, which will replace the existing scale.
Supplementary Figure 1
Demographics plot (Figure S1A)
options(timeout = 3600)
if (!file.exists("demographics.qs")) {
download.file(
"https://zenodo.org/records/14226219/files/umap_figure.qs?download=1",
"demographics.qs"
)
}
demographics <- qread("demographics.qs")
plotDemographics <- function(var) {
plot <- demographics |>
dplyr::filter(!is.na(.data[[var]])) |>
ggplot(aes(x = level2, y = .data[[var]], fill = level2)) +
geom_boxplot() +
geom_point() +
theme_bw() +
scale_fill_manual(values = umap_figure@misc$level2_cols) +
xlab("") +
ylab("") +
ggtitle(var) +
theme(legend.position = "none")
print(plot)
}
purrr::walk(c("age", "ncv_tibial_motoric", "incat"), plotDemographics)
demographics |>
ggplot(aes(x = level2, fill = sex)) +
geom_bar() +
theme_bw() +
scale_fill_manual(values = pals::cols25(2)) +
xlab("") +
ylab("") +
ggtitle("sex")
UMAP grouped by center and sample (Figure S1C)
DimPlot(
umap_figure,
reduction = "umap.scvi.full",
pt.size = .1,
raster = FALSE,
alpha = 0.1,
group.by = "center",
cols = pals::cols25(3),
label = FALSE
) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
) +
xlab("UMAP_1") +
ylab("UMAP_2")
my_cols_50 <- unname(Polychrome::createPalette(50, pals::cols25()))
DimPlot(
umap_figure,
reduction = "umap.scvi.full",
pt.size = .1,
raster = FALSE,
alpha = 0.1,
group.by = "sample",
cols = umap_figure@misc$sample_cols,
label = FALSE
) +
theme(
axis.text = element_blank(),
axis.ticks = element_blank()
) +
xlab("UMAP_1") +
ylab("UMAP_2")
Number of genes per nucleus (Figure S1D)
count_genes <- dplyr::tibble(
feature = umap_figure@meta.data$nFeature_RNA,
sample = umap_figure@meta.data$sample
)
count_genes |>
ggplot(aes(x = sample, y = feature, fill = sample)) +
geom_boxplot() +
scale_fill_manual(values = umap_figure@misc$sample_cols) +
theme_classic() +
theme(legend.position = "none") +
xlab("") +
ylab("") +
ggtitle("Number of genes per nucleus") +
theme(axis.text.x = element_text(angle = 90))
Session info
session_info()─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.3.1 (2023-06-16)
os Ubuntu 22.04.5 LTS
system x86_64, linux-gnu
ui X11
language (EN)
collate C.UTF-8
ctype C.UTF-8
tz UTC
date 2024-12-21
pandoc 2.19.2 @ /usr/local/bin/ (via rmarkdown)
─ Packages ───────────────────────────────────────────────────────────────────
package * version date (UTC) lib source
abind 1.4-5 2016-07-21 [1] RSPM (R 4.3.0)
Augur 1.0.3 2024-12-20 [1] Github (neurorestore/Augur@fa5d837)
backports 1.4.1 2021-12-13 [1] RSPM (R 4.3.0)
beachmat 2.18.0 2023-10-24 [1] Bioconductor
beeswarm 0.4.0 2021-06-01 [1] RSPM (R 4.3.0)
Biobase 2.62.0 2023-10-24 [1] Bioconductor
BiocGenerics 0.48.1 2023-11-01 [1] Bioconductor
BiocNeighbors 1.20.2 2024-01-07 [1] Bioconductor 3.18 (R 4.3.1)
BiocParallel 1.36.0 2023-10-24 [1] Bioconductor
BiocSingular 1.18.0 2023-10-24 [1] Bioconductor
bitops 1.0-7 2021-04-24 [1] RSPM (R 4.3.0)
broom 1.0.5 2023-06-09 [1] RSPM (R 4.3.0)
car 3.1-2 2023-03-30 [1] RSPM (R 4.3.0)
carData 3.0-5 2022-01-06 [1] RSPM (R 4.3.0)
class 7.3-22 2023-05-03 [1] CRAN (R 4.3.1)
cli 3.6.2 2023-12-11 [1] RSPM (R 4.3.0)
cluster 2.1.6 2023-12-01 [1] RSPM (R 4.3.0)
codetools 0.2-19 2023-02-01 [1] CRAN (R 4.3.1)
colorspace 2.1-0 2023-01-23 [1] RSPM (R 4.3.0)
cowplot 1.1.2 2023-12-15 [1] RSPM (R 4.3.0)
crayon 1.5.2 2022-09-29 [1] RSPM (R 4.3.0)
data.table 1.14.10 2023-12-08 [1] RSPM (R 4.3.0)
DelayedArray 0.28.0 2023-10-24 [1] Bioconductor
DelayedMatrixStats 1.24.0 2023-10-24 [1] Bioconductor
deldir 2.0-2 2023-11-23 [1] RSPM (R 4.3.0)
dichromat 2.0-0.1 2022-05-02 [1] RSPM (R 4.3.0)
digest 0.6.34 2024-01-11 [1] RSPM (R 4.3.0)
dotCall64 1.1-1 2023-11-28 [1] RSPM (R 4.3.0)
dplyr * 1.1.4 2023-11-17 [1] RSPM (R 4.3.0)
edgeR 4.0.7 2024-01-12 [1] Bioconductor 3.18 (R 4.3.1)
ellipsis 0.3.2 2021-04-29 [1] RSPM (R 4.3.0)
EnhancedVolcano * 1.20.0 2023-10-24 [1] Bioconductor
evaluate 0.23 2023-11-01 [1] RSPM (R 4.3.0)
fansi 1.0.6 2023-12-08 [1] RSPM (R 4.3.0)
farver 2.1.1 2022-07-06 [1] RSPM (R 4.3.0)
fastDummies 1.7.3 2023-07-06 [1] RSPM (R 4.3.0)
fastmap 1.1.1 2023-02-24 [1] RSPM (R 4.3.0)
fitdistrplus 1.1-11 2023-04-25 [1] RSPM (R 4.3.0)
forcats * 1.0.0 2023-01-29 [1] RSPM (R 4.3.0)
furrr 0.3.1 2022-08-15 [1] RSPM (R 4.3.0)
future 1.33.1 2023-12-22 [1] RSPM (R 4.3.0)
future.apply 1.11.1 2023-12-21 [1] RSPM (R 4.3.0)
generics 0.1.3 2022-07-05 [1] RSPM (R 4.3.0)
GenomeInfoDb 1.38.5 2023-12-28 [1] Bioconductor 3.18 (R 4.3.1)
GenomeInfoDbData 1.2.11 2024-12-20 [1] Bioconductor
GenomicRanges 1.54.1 2023-10-29 [1] Bioconductor
ggbeeswarm 0.7.2 2023-04-29 [1] RSPM (R 4.3.0)
ggforce 0.4.1 2022-10-04 [1] RSPM (R 4.3.0)
ggplot2 * 3.5.1 2024-04-23 [1] RSPM (R 4.3.0)
ggpubr 0.6.0 2023-02-10 [1] RSPM (R 4.3.0)
ggraph 2.1.0 2022-10-09 [1] RSPM (R 4.3.0)
ggrepel * 0.9.5 2024-01-10 [1] RSPM (R 4.3.0)
ggridges 0.5.5 2023-12-15 [1] RSPM (R 4.3.0)
ggsignif 0.6.4 2022-10-13 [1] RSPM (R 4.3.0)
globals 0.16.2 2022-11-21 [1] RSPM (R 4.3.0)
glue 1.7.0 2024-01-09 [1] RSPM (R 4.3.0)
goftest 1.2-3 2021-10-07 [1] RSPM (R 4.3.0)
gower 1.0.1 2022-12-22 [1] RSPM (R 4.3.0)
graphlayouts 1.0.2 2023-11-03 [1] RSPM (R 4.3.0)
gridExtra 2.3 2017-09-09 [1] RSPM (R 4.3.0)
gtable 0.3.4 2023-08-21 [1] RSPM (R 4.3.0)
gtools 3.9.5 2023-11-20 [1] RSPM (R 4.3.0)
hardhat 1.3.0 2023-03-30 [1] RSPM (R 4.3.0)
hms 1.1.3 2023-03-21 [1] RSPM (R 4.3.0)
htmltools 0.5.7 2023-11-03 [1] RSPM (R 4.3.0)
htmlwidgets 1.6.4 2023-12-06 [1] RSPM (R 4.3.0)
httpuv 1.6.13 2023-12-06 [1] RSPM (R 4.3.0)
httr 1.4.7 2023-08-15 [1] RSPM (R 4.3.0)
ica 1.0-3 2022-07-08 [1] RSPM (R 4.3.0)
igraph 1.6.0 2023-12-11 [1] RSPM (R 4.3.0)
ipred 0.9-14 2023-03-09 [1] RSPM (R 4.3.0)
IRanges 2.36.0 2023-10-24 [1] Bioconductor
irlba 2.3.5.1 2022-10-03 [1] RSPM (R 4.3.0)
jsonlite 1.8.8 2023-12-04 [1] RSPM (R 4.3.0)
KernSmooth 2.23-22 2023-07-10 [1] RSPM (R 4.3.0)
knitr 1.45 2023-10-30 [1] RSPM (R 4.3.0)
labeling 0.4.3 2023-08-29 [1] RSPM (R 4.3.0)
later 1.3.2 2023-12-06 [1] RSPM (R 4.3.0)
lattice 0.22-5 2023-10-24 [1] RSPM (R 4.3.0)
lava 1.7.3 2023-11-04 [1] RSPM (R 4.3.0)
lazyeval 0.2.2 2019-03-15 [1] RSPM (R 4.3.0)
leiden 0.4.3.1 2023-11-17 [1] RSPM (R 4.3.0)
lifecycle 1.0.4 2023-11-07 [1] RSPM (R 4.3.0)
limma 3.58.1 2023-10-31 [1] Bioconductor
listenv 0.9.0 2022-12-16 [1] RSPM (R 4.3.0)
lmtest 0.9-40 2022-03-21 [1] RSPM (R 4.3.0)
locfit 1.5-9.8 2023-06-11 [1] RSPM (R 4.3.0)
lubridate * 1.9.3 2023-09-27 [1] RSPM (R 4.3.0)
magrittr 2.0.3 2022-03-30 [1] RSPM (R 4.3.0)
mapproj 1.2.11 2023-01-12 [1] RSPM (R 4.3.0)
maps 3.4.2 2023-12-15 [1] RSPM (R 4.3.0)
MASS 7.3-60 2023-05-04 [1] CRAN (R 4.3.1)
Matrix 1.6-5 2024-01-11 [1] RSPM (R 4.3.0)
MatrixGenerics 1.15.0 2024-12-20 [1] Github (Bioconductor/MatrixGenerics@ed066a2)
matrixStats 1.2.0 2023-12-11 [1] RSPM (R 4.3.0)
miloDE * 0.0.0.9000 2024-12-20 [1] Github (MarioniLab/milODE@3f825b9)
miloR 1.10.0 2023-10-24 [1] Bioconductor
mime 0.12 2021-09-28 [1] RSPM (R 4.3.0)
miniUI 0.1.1.1 2018-05-18 [1] RSPM (R 4.3.0)
munsell 0.5.0 2018-06-12 [1] RSPM (R 4.3.0)
nlme 3.1-164 2023-11-27 [1] RSPM (R 4.3.0)
nnet 7.3-19 2023-05-03 [1] CRAN (R 4.3.1)
pals * 1.8 2023-08-23 [1] RSPM (R 4.3.0)
parallelly 1.36.0 2023-05-26 [1] RSPM (R 4.3.0)
parsnip 1.1.1 2023-08-17 [1] RSPM (R 4.3.0)
patchwork 1.2.0 2024-01-08 [1] RSPM (R 4.3.0)
pbapply 1.7-2 2023-06-27 [1] RSPM (R 4.3.0)
pbmcapply 1.5.1 2022-04-28 [1] RSPM (R 4.3.0)
pillar 1.9.0 2023-03-22 [1] RSPM (R 4.3.0)
pkgconfig 2.0.3 2019-09-22 [1] RSPM (R 4.3.0)
plotly 4.10.3 2023-10-21 [1] RSPM (R 4.3.0)
plyr 1.8.9 2023-10-02 [1] RSPM (R 4.3.0)
png 0.1-8 2022-11-29 [1] RSPM (R 4.3.0)
polyclip 1.10-6 2023-09-27 [1] RSPM (R 4.3.0)
prodlim 2023.08.28 2023-08-28 [1] RSPM (R 4.3.0)
progressr 0.14.0 2023-08-10 [1] RSPM (R 4.3.0)
promises 1.2.1 2023-08-10 [1] RSPM (R 4.3.0)
purrr * 1.0.2 2023-08-10 [1] RSPM (R 4.3.0)
qs * 0.25.7 2023-12-06 [1] RSPM (R 4.3.0)
R6 2.5.1 2021-08-19 [1] RSPM (R 4.3.0)
randomForest 4.7-1.1 2022-05-23 [1] RSPM (R 4.3.0)
RANN 2.6.1 2019-01-08 [1] RSPM (R 4.3.0)
RApiSerialize 0.1.2 2022-08-25 [1] RSPM (R 4.3.0)
RColorBrewer 1.1-3 2022-04-03 [1] RSPM (R 4.3.0)
Rcpp 1.0.12 2024-01-09 [1] RSPM (R 4.3.0)
RcppAnnoy 0.0.21 2023-07-02 [1] RSPM (R 4.3.0)
RcppGreedySetCover 0.1.0 2018-01-24 [1] RSPM (R 4.3.0)
RcppHNSW 0.5.0 2023-09-19 [1] RSPM (R 4.3.0)
RcppParallel 5.1.7 2023-02-27 [1] RSPM (R 4.3.0)
RCurl 1.98-1.14 2024-01-09 [1] RSPM (R 4.3.0)
readr * 2.1.5 2024-01-10 [1] RSPM (R 4.3.0)
recipes 1.0.9 2023-12-13 [1] RSPM (R 4.3.0)
reshape2 1.4.4 2020-04-09 [1] RSPM (R 4.3.0)
reticulate 1.34.0 2023-10-12 [1] RSPM (R 4.3.0)
rlang 1.1.3 2024-01-10 [1] RSPM (R 4.3.0)
rmarkdown 2.25 2023-09-18 [1] RSPM (R 4.3.0)
ROCR 1.0-11 2020-05-02 [1] RSPM (R 4.3.0)
rpart 4.1.23 2023-12-05 [1] RSPM (R 4.3.0)
rsample 1.2.0 2023-08-23 [1] RSPM (R 4.3.0)
RSpectra 0.16-1 2022-04-24 [1] RSPM (R 4.3.0)
rstatix 0.7.2 2023-02-01 [1] RSPM (R 4.3.0)
rsvd 1.0.5 2021-04-16 [1] RSPM (R 4.3.0)
Rtsne 0.17 2023-12-07 [1] RSPM (R 4.3.0)
S4Arrays 1.2.0 2023-10-24 [1] Bioconductor
S4Vectors 0.40.2 2023-11-23 [1] Bioconductor 3.18 (R 4.3.1)
ScaledMatrix 1.10.0 2023-10-24 [1] Bioconductor
scales 1.3.0 2023-11-28 [1] RSPM (R 4.3.0)
scattermore 1.2 2023-06-12 [1] RSPM (R 4.3.0)
scMisc * 0.3.0 2024-12-20 [1] Github (mihem/scMisc@6b1e979)
sctransform 0.4.1 2023-10-19 [1] RSPM (R 4.3.0)
scuttle 1.12.0 2023-10-24 [1] Bioconductor
sessioninfo * 1.2.2 2021-12-06 [1] RSPM (R 4.3.0)
Seurat * 5.0.1 2024-12-20 [1] Github (fentouxungui/seurat@d2d8d60)
SeuratObject * 5.0.1 2023-11-17 [1] RSPM (R 4.3.0)
shiny 1.8.0 2023-11-17 [1] RSPM (R 4.3.0)
SingleCellExperiment 1.24.0 2023-10-24 [1] Bioconductor
sp * 2.1-2 2023-11-26 [1] RSPM (R 4.3.0)
spam 2.10-0 2023-10-23 [1] RSPM (R 4.3.0)
SparseArray 1.2.3 2023-12-25 [1] Bioconductor 3.18 (R 4.3.1)
sparseMatrixStats 1.13.4 2024-12-20 [1] Github (const-ae/sparseMatrixStats@4c6e814)
spatstat.data 3.0-3 2023-10-24 [1] RSPM (R 4.3.0)
spatstat.explore 3.2-5 2023-10-22 [1] RSPM (R 4.3.0)
spatstat.geom 3.2-7 2023-10-20 [1] RSPM (R 4.3.0)
spatstat.random 3.2-2 2023-11-29 [1] RSPM (R 4.3.0)
spatstat.sparse 3.0-3 2023-10-24 [1] RSPM (R 4.3.0)
spatstat.utils 3.0-4 2023-10-24 [1] RSPM (R 4.3.0)
statmod 1.5.0 2023-01-06 [1] RSPM (R 4.3.0)
stringfish 0.16.0 2023-11-28 [1] RSPM (R 4.3.0)
stringi 1.8.3 2023-12-11 [1] RSPM (R 4.3.0)
stringr * 1.5.1 2023-11-14 [1] RSPM (R 4.3.0)
SummarizedExperiment 1.32.0 2023-10-24 [1] Bioconductor
survival 3.5-7 2023-08-14 [1] RSPM (R 4.3.0)
tensor 1.5 2012-05-05 [1] RSPM (R 4.3.0)
tester 0.1.7 2013-11-14 [1] RSPM (R 4.3.0)
tibble * 3.2.1 2023-03-20 [1] RSPM (R 4.3.0)
tidygraph 1.3.0 2023-12-18 [1] RSPM (R 4.3.0)
tidyr * 1.3.0 2023-01-24 [1] RSPM (R 4.3.0)
tidyselect 1.2.0 2022-10-10 [1] RSPM (R 4.3.0)
tidyverse * 2.0.0 2023-02-22 [1] RSPM (R 4.3.0)
timechange 0.2.0 2023-01-11 [1] RSPM (R 4.3.0)
timeDate 4032.109 2023-12-14 [1] RSPM (R 4.3.0)
tweenr 2.0.2 2022-09-06 [1] RSPM (R 4.3.0)
tzdb 0.4.0 2023-05-12 [1] RSPM (R 4.3.0)
utf8 1.2.4 2023-10-22 [1] RSPM (R 4.3.0)
uwot 0.1.16 2023-06-29 [1] RSPM (R 4.3.0)
vctrs 0.6.5 2023-12-01 [1] RSPM (R 4.3.0)
vipor 0.4.7 2023-12-18 [1] RSPM (R 4.3.0)
viridis 0.6.4 2023-07-22 [1] RSPM (R 4.3.0)
viridisLite 0.4.2 2023-05-02 [1] RSPM (R 4.3.0)
withr 2.5.2 2023-10-30 [1] RSPM (R 4.3.0)
xfun 0.41 2023-11-01 [1] RSPM (R 4.3.0)
xtable 1.8-4 2019-04-21 [1] RSPM (R 4.3.0)
XVector 0.42.0 2023-10-24 [1] Bioconductor
yaml 2.3.8 2023-12-11 [1] RSPM (R 4.3.0)
yardstick 1.2.0 2023-04-21 [1] RSPM (R 4.3.0)
zlibbioc 1.48.0 2023-10-24 [1] Bioconductor
zoo 1.8-12 2023-04-13 [1] RSPM (R 4.3.0)
[1] /opt/R/4.3.1/lib/R/library
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